芳香羧酸衍生物驱避剂的非线性定量构效关系

Nonlinear quantitative structure-activity relationship of the aromatic carboxylic acid repellents

【目的】驱避剂可使害虫不敢接近受用者从而保护受用者免遭其害。建立高精度、可解释性强的非线性定量构效关系(quantitative structure-activity relationship,QSAR)模型对设计合成新的高效昆虫驱避剂有重要意义。【方法】基于37个芳香羧酸类化合物对家蝇Musca domestica的驱避活性,以量子化学计算软件PCLIENT获取每一化合物初始描述符,以二元矩阵重排过滤器、多轮末尾淘汰实施特征非线性筛选,以支持向量回归(support vector regression,SVR)建立非线性QSAR模型,以SVR非线性解释体系分析各保留描述符对驱避活性的影响。【结果】1 542个初始描述符的SVR模型F=1.2,特征筛选后6个保留描述符的SVR模型F=184.6,特征筛选对QSAR模型精度有重要影响。6个保留分子描述符的重要性依次为p4BCD>GATS7v>T(O..O)>JGI8>SssO>nArCONR2。【结论】保留描述符与芳香羧酸类化合物对家蝇驱避活性的非线性关系明显,获得了高精度、普适性强的非线性SVR-QSAR模型。

【Aim】Repellent can protect the users by driving target pests away from them. It is important to establish a nonlinear quantitative structure-activity relationship（ QSAR） model with high precision and strong interpretation for designing and synthesizing the new insect repellent with higher bioactivity.【Methods】Based on the repellent activities of 37 aromatic carboxylic acid derivatives against the housefly,Musca domestica,the initial descriptors were generated with stoichiometry software PCLIENT,and then the binary matrix shuffling filter（ BMSF） and worst descriptor elimination multi-round method（ WDEM） were successively used to conduct the nonlinear selection for initial descriptors. With the reserved descriptors,a support vector regression（ SVR） model was established for the QSAR analysis of these 37 repellent derivatives. The influence of reserved descriptors on repellent activities was further analyzed with SVR interpretation system. 【Results】The F-score of SVR model with original 1 542 descriptors was 1. 2. However,it was 184. 6 with the retained six descriptors after feature screening,indicating that feature screening has important effects on the precision of QSAR model. The importance of six molecular descriptors was as follows： p4 BCD 〉 GATS7 v 〉 T（ O.. O） 〉 JGI8 〉 SssO 〉 nArCONR2.【Conclusion】The nonlinear relationship between reserved descriptors and the repellent activities of aromatic carboxylic acid derivatives against M. domestica was remarkable,and a high-performance SVRQSAR model for repellent derivatives was constructed.